In PyTorch environment [1]}, all deep learning experiments were conducted with a computer system equipped with two Intel Xeon CPUs E5-2630 v4, 128GB DDR4 RAM, and four NVIDIA GeForce GTX 2080ti GPUs. The optimization was conducted using Adam optimizer [2]} and multi-GPUs. Batch normalization was applied to achieve fast convergence and minimization [3]}. The network capacity (i.e., feature and network depths) was minimized as much as possible while maintaining the backbone structure because of the huge computational cost associated with the CBCT image size of \(800\times 800\times 400\) .
